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The hydrological system of thebasin of Lake Urmia is complex, deriving its supply from a network comprising 13 perennial rivers, along withnumerous small springs and direct precipitation onto the lake’s surface. Among these contributors, approximately half of the inflow is attributed to the Zarrineh River and the Simineh River. Remarkably, Lake Urmia lacks a natural outlet, with its water loss occurring solely through evaporation processes. This study employed a comprehensive methodology integrating ground surveys, remote sensing analyses, and meticulous documentation of historical landslides within the basin as primary information sources. Through this investigative approach, we preciselyidentified and geolocated a total of 512 historical landslide occurrences across the Urmia Lake drainage basin, leveraging GPS technology for precision. Thisarticle introduces a suite of hybrid machine learning predictive models, such as support-vector machine (SVM), random forest (RF), decision trees (DT), logistic regression (LR), fuzzy logic (FL), and the technique for order of preference by similarity to the ideal solution (TOPSIS). These models were strategically deployed to assess landslide susceptibility within the region. The outcomes of the landslide susceptibility assessment reveal that the main high susceptible zones for landslide occurrence are concentrated in the northwestern, northern, northeastern, and some southern and southeastern areas of the region. Moreover, when considering the implementation of predictions using different algorithms, it became evident that SVM exhibited superior performance regardingboth accuracy (0.89) and precision (0.89), followed by RF, with and accuracy of 0.83 and a precision of 0.83. However, it is noteworthy that TOPSIS yielded the lowest accuracy value among the algorithms assessed. Full article

Many saline lakes throughout the world are shrinking due to overexploitation of water in their drainage basins. Among them are two of the world’s largest saline lakes, the U.S.A.’s Great Salt Lake, and Iran’s Lake Urmia. Here we provide a comparative analysis of the desiccation of these two lakes that provides insights on management decisions that may help save them and that are relevant to saline lake management worldwide. Great Salt Lake and Lake Urmia were once remarkably similar in size, depth, salinity, and geographic setting. High rates of population growth in both basins have fueled a demand for irrigated agriculture and other uses. In the Great Salt Lake basin, this development began in the late 1800’s and is continuing. The lake’s volume has decreased by 67%, with 75% of the loss driven by water development and 25% by a millennial drought which may portend the start of global climate change impacts. This has greatly increased salinities to 180 g·L⁻¹ stressing the invertebrates in the lake on which birds depend. Only 1% of people in the basin are employed in agriculture; thus, reducing the demand for irrigation development. Population densities in the Urmia basin are double those of the Great Salt Lake basin, and 28% of people are employed in agriculture. These demographics have led to a rapid increase in reservoir construction since 2000 and the subsequent loss of 87% of Lake Urmia’s volume. The water development of Lake Urmia was later, but much faster than that of Great Salt Lake, causing Urmia’s salinity to increase from 190 to over 350 g·L⁻¹ in just 20 years, with subsequent severe ecological decline. Dust storms from the exposed lakebeds of both systems threaten the health of the surrounding populations. To save these lakes and others will require: (1) transparent and collaborative involvement with local interest groups; (2) shifts away from an agricultural-based economy to one based on manufacturing and services; (3) consideration of the diverse ecosystem services of the lakes including mineral extraction, recreation, bird habitats in surrounding wetlands, and dust control. Full article